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inputs stringlengths 0 3.3M	targets stringlengths 0 3.3M	dataset stringlengths 6 103
Interact with a household to solve a task. Imagine you are an intelligent agent in a household environment and your target is to perform actions to complete the task goal. At the beginning of your interactions, you will be given the detailed description of the current environment and your goal to accomplish. For each of your turn, you will be given a list of actions which you can choose one to perform in this turn. You should choose from two actions: "THOUGHT" or "ACTION". If you choose "THOUGHT", you should first think about the current condition and plan for your future actions, and then output your action in this turn. Your output must strictly follow this format:"THOUGHT: your thoughts. ACTION: your next action "; If you choose "ACTION", you should directly output the action in this turn. Your output must strictly follow this format:"ACTION: your next action ". After your each turn, the environment will give you immediate feedback based on which you plan your next few steps. if the envrionment output "Nothing happened", that means the previous action is invalid and you should try more options. Reminder: 1. the action must be chosen from the given available actions. Any actions except provided available actions will be regarded as illegal. 2. Think when necessary, try to act directly more in the process.	OK. I'll follow your instructions and try my best to solve the task.	AgentInstruct-alfworld
Here is your task. You are in the middle of a room. Looking quickly around you, you see a armchair 2, a armchair 1, a bed 1, a countertop 1, a diningtable 1, a drawer 2, a drawer 1, a dresser 1, a garbagecan 1, a laundryhamper 1, and a sidetable 1. Your task is to: find two laptop and put them in bed.	THOUGHT: I need to find two laptops and put them in bed. I'll begin by searching the areas that are most likely to have laptops. ACTION: go to diningtable 1	AgentInstruct-alfworld
On the diningtable 1, you see a alarmclock 2, a bowl 2, a cd 2, a creditcard 2, a creditcard 1, a laptop 1, a mug 2, a pencil 1, and a remotecontrol 2.	ACTION: take laptop 1 from diningtable 1	AgentInstruct-alfworld
You pick up the laptop 1 from the diningtable 1.	ACTION: go to bed 1	AgentInstruct-alfworld
On the bed 1, you see a pillow 2, and a pillow 1.	ACTION: put laptop 1 in/on bed 1	AgentInstruct-alfworld
You put the laptop 1 in/on the bed 1.	ACTION: go to countertop 1	AgentInstruct-alfworld
On the countertop 1, you see a alarmclock 3, a bowl 3, a cellphone 3, a creditcard 3, and a mirror 1.	ACTION: go to drawer 1	AgentInstruct-alfworld
The drawer 1 is closed.	ACTION: open drawer 1	AgentInstruct-alfworld
You open the drawer 1. The drawer 1 is open. In it, you see a pen 2.	ACTION: go to drawer 2	AgentInstruct-alfworld
The drawer 2 is closed.	ACTION: open drawer 2	AgentInstruct-alfworld
You open the drawer 2. The drawer 2 is open. In it, you see a book 1, and a keychain 2.	ACTION: go to dresser 1	AgentInstruct-alfworld
On the dresser 1, you see a bowl 1, a cd 1, a cellphone 1, a keychain 1, a pen 1, a remotecontrol 1, and a television 1.	ACTION: go to armchair 1	AgentInstruct-alfworld
On the armchair 1, you see a laptop 2.	ACTION: take laptop 2 from armchair 1	AgentInstruct-alfworld
You pick up the laptop 2 from the armchair 1.	ACTION: go to bed 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: put laptop 2 in/on bed 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: go to shelf 5	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: cool plate 1 with fridge 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: put cup 1 in/on cabinet 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	THOUGHT: There might be a spray bottle in the garbage can. It does seem less likely, but still possible. ACTION: go to garbagecan 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: take cup 2 from countertop 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: take spraybottle 1 from garbagecan 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: put spoon 1 in/on sidetable 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: cool potato 2 with fridge 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	THOUGHT: There seems to be a misunderstanding. The objective was to put a cellphone in the dresser, but no cellphone was specified in the initial room inventory. It's also nowhere to be found in the room. ACTION: look	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: go to stoveburner 3	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: put knife 1 in/on sidetable 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: put cup 2 in/on shelf 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	THOUGHT: The cellphone is not in any of the drawers, desks or dressers. It may be in the cabinet. I need to check there next. ACTION: go to cabinet 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	THOUGHT: There's no second cellphone on shelf 5 either. I still haven't checked shelf 6 and 7. I need to quickly move over to shelf 6. ACTION: go to shelf 6	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: go to microwave 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: use desklamp 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: take potato 1 from countertop 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: take keychain 1 from dresser 2	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: go to diningtable 2	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: put mug 1 in/on coffeemachine 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: take mug 1 from sidetable 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: go to microwave 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: take cellphone 1 from sidetable 1	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: go to cabinet 11	AgentInstruct-alfworld
On the drawer 7, you see nothing.	ACTION: put pen 2 in/on desk 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to diningtable 2	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	THOUGHT: The keychain could be in the garbage can as well. I should check there. ACTION: go to garbagecan 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to cabinet 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 4	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open cabinet 2	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: put spraybottle 1 in/on shelf 3	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 8	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: take book 2 from bed 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to sinkbasin 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: cool bowl 2 with fridge 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to desk 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 3	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 2	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 8	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: take cd 1 from diningtable 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: take cup 1 from countertop 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	THOUGHT: The spraybottle is not in drawer 2 either. I will check the next drawer. ACTION: go to drawer 3	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to cabinet 6	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to toilet 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open cabinet 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to shelf 11	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	THOUGHT: The spraybottles are missing in the current environment. Hence, I'm unable to perform the task. ACTION: end task	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open cabinet 6	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to cabinet 6	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: take book 2 from bed 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to cabinet 5	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to cabinet 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: put candle 1 in/on countertop 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 3	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 3	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: put glassbottle 2 in/on fridge 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open cabinet 8	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	THOUGHT: Apparently there was some confusion about the pen 1 going to the desk 1. I should try to get it again and put it on the desk. ACTION: take pen 2 from desk 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 3	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to fridge 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: put potato 1 in/on fridge 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to fridge 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to sofa 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: put cd 1 in/on sidetable 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open cabinet 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: put cellphone 3 in/on diningtable 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 6	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 5	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to drawer 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: go to toiletpaperhanger 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	THOUGHT: Not sure why I picked up the box from the armchair. Seems like a mistake. Better put it back and complete the task of placing the remotecontrol 1 on the armchair. ACTION: put box 1 in/on armchair 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	THOUGHT: There is no tomato on countertop 1, let's check countertop 2. ACTION: go to countertop 2	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open drawer 7	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: clean soapbar 1 with sinkbasin 1	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open cabinet 6	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: open cabinet 8	AgentInstruct-alfworld
On the bed 1, you see a laptop 1, a pillow 2, and a pillow 1.	ACTION: take soapbottle 2 from cabinet 4	AgentInstruct-alfworld

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YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

Dataset Card for Data Provenance Initiative - Commercial-Licenses-and-Terms

Legal Disclaimer / Notice

Collected License Information is NOT Legal Advice. It is important to note we collect self-reported licenses, from the papers and repositories that released these datasets, and categorize them according to our best efforts, as a volunteer research and transparency initiative. The information provided by any of our works and any outputs of the Data Provenance Initiative do not, and are not intended to, constitute legal advice; instead, all information, content, and materials are for general informational purposes only. Readers and users should seek their own legal advice from counsel in their relevant jurisdiction.

Dataset Summary

A wave of recent language models have been powered by large collections of natural language datasets. The sudden race to train models on these disparate collections of incorrectly, ambiguously, or under-documented datasets has left practitioners unsure of the legal and qualitative characteristics of the models they train. To remedy this crisis in data transparency and understanding, in a joint effort between experts in machine learning and the law, we’ve compiled the most detailed and reliable metadata available for data licenses, sources, and provenance, as well as fine-grained characteristics like language, text domains, topics, usage, collection time, and task compositions. Beginning with nearly 40 popular instruction (or “alignment”) tuning collections, we release a suite of open source tools for downloading, filtering, and examining this training data. Our analysis sheds light on the fractured state of data transparency, particularly with data licensing, and we hope our tools will empower more informed and responsible data-centric development of future language models.

What does All-Licenses mean here?

All-Licenses includes all datasets from Data-Provenance-Initiative which includes datasets with licenses that limit their usage to academic or non-profit uses as well as datasets for which licensing information could not be identified

Constituent Data Collections

Following table shows each constituent data collection this Dataset along with original source from where each data collection is derived from.
Following table shows each constituent data collection this Dataset along with original source from where each data collection is derived from.

#	Collection Name	Description	Source
1	AgentInstruct	AgentInstruct is a meticulously curated dataset featuring 1,866 high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like Task Derivation and Self-Instruct.	https://huggingface.co/datasets/THUDM/AgentInstruct
2	Anthropic HH-RLHF	Human preference data about helpfulness and harmlessness & Human-generated and annotated red teaming dialogues.	https://huggingface.co/datasets/Anthropic/hh-rlhf
3	Aya Dataset	The `Aya Dataset` is a multilingual instruction fine-tuning dataset curated by an open-science community via Aya Annotation Platform from Cohere For AI. The dataset contains a total of 204k human-annotated prompt-completion pairs along with the demographics data of the annotators.	https://huggingface.co/datasets/CohereForAI/aya_dataset
4	COIG	We propose the Chinese Open Instruction Generalist (COIG) project to maintain a harmless, helpful, and diverse set of Chinese instruction corpora.	https://huggingface.co/datasets/BAAI/COIG
5	Capybara	Capybara is the culmination of insights derived from synthesis techniques like Evol-instruct (used for WizardLM), Alpaca, Orca, Vicuna, Lamini, FLASK and others. The single-turn seeds used to initiate the Amplify-Instruct synthesis of conversations are mostly based on datasets that i've personally vetted extensively, and are often highly regarded for their diversity and demonstration of logical robustness and prose, such as Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from different sources, including certain in-house multi-turn datasets like Dove and Verified-Camel(A successor to Puffin)	https://huggingface.co/datasets/LDJnr/Capybara
6	CommitPackFT	CommitPackFT is a 2GB filtered version of CommitPack to contain only high-quality commit messages that resemble natural language instructions.	https://huggingface.co/datasets/bigcode/commitpackft
7	DialogStudio	Unified Dialog Datasets and Instruction-Aware Models for Conversational AI	https://huggingface.co/datasets/Salesforce/dialogstudio
8	Dolly 15k	Databricks Dolly 15k is a dataset containing 15,000 high-quality human-generated prompt / response pairs specifically designed for instruction tuning large language models.	https://huggingface.co/datasets/databricks/databricks-dolly-15k
9	Dynosaur	BLiMP (dynosaur-full) is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars. Aggregate human agreement with the labels is 96.4%. We use BLiMP to evaluate an n-gram LM, LSTM LM, GPT-2, and Transformer-XL.	https://huggingface.co/datasets/Dynosaur/dynosaur-full
10	Flan Collection (Chain-of-Thought)	Chain-of-Thought sub-mixture in Flan collection dataset.	https://huggingface.co/datasets/conceptofmind/cot_submix_original
11	Flan Collection (Dialog)	Chain-of-Thought sub-mixture in Flan collection dataset.	https://huggingface.co/datasets/conceptofmind/dialog_submix_original
12	Flan Collection (Flan 2021)	Flan 2021 sub-mixture in Flan collection dataset.	https://huggingface.co/datasets/conceptofmind/flan2021_submix_original
13	Flan Collection (P3)	P3 sub-mixture in Flan collection dataset.	https://huggingface.co/datasets/conceptofmind/t0_submix_original
14	Flan Collection (Super-NaturalInstructions)	Super-Natural Instructions in Flan collection dataset.	https://huggingface.co/datasets/conceptofmind/niv2_submix_original
15	Glaive Code Assistant	Glaive-code-assistant-v2 is a dataset of ~215k code problems and solutions generated using Glaive's synthetic data generation platform.	https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v2
16	Gretel Text-to-SQL	gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples, designed and generated using Gretel Navigator	https://huggingface.co/datasets/gretelai/synthetic_text_to_sql
17	HelpSteer	HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses.	https://huggingface.co/datasets/nvidia/HelpSteer
18	Indic-Instruct	A collection of different instruction datasets spanning English and Hindi languages.	https://huggingface.co/datasets/ai4bharat/indic-instruct-data-v0.1
19	Joke Explanation	Corpus for testing whether your LLM can explain the joke well.	https://huggingface.co/datasets/theblackcat102/joke_explaination
20	Medical Meadow	This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams."	https://huggingface.co/datasets/medalpaca/medical_meadow_medqa
21	MegaWika	MegaWika is a multi- and crosslingual text dataset containing 30 million Wikipedia passages with their scraped and cleaned web citations. The passages span 50 Wikipedias in 50 languages, and the articles in which the passages were originally embedded are included for convenience	https://huggingface.co/datasets/hltcoe/megawika
22	OIG	Open Instruction Generalist is a large instruction dataset of medium quality along with a smaller high quality instruction dataset (OIG-small-chip2).	https://huggingface.co/datasets/laion/OIG
23	Open Assistant	OpenAssistant Conversations (OASST1) is a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees.	https://huggingface.co/datasets/OpenAssistant/oasst1
24	Open Assistant OctoPack	Filtered version of OpenAssistant Conversations (OASST1) to focus only on high-quality conversation trees as used in OctoPack paper.	https://huggingface.co/datasets/bigcode/oasst-octopack
25	Open Assistant v2
26	Open-Platypus
27	OpenMathInstruct-1	OpenMathInstruct-1 is a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed Mixtral-8x7B model.	https://huggingface.co/datasets/nvidia/OpenMathInstruct-1
28	PygmalionAI-PIPPA	Personal Interaction Pairs between People and AI (PIPPA) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project.	https://huggingface.co/datasets/PygmalionAI/PIPPA
29	RiddleSense	We present RiddleSense, a new multiple-choice question answering task, which comes with the first large dataset (5.7k examples) for answering riddle-style commonsense questions.	https://huggingface.co/datasets/INK-USC/riddle_sense
30	SEACrowd	The first online catalogue for SEACrowd datasheets. This catalogue contains 498 datasets with metadata annotations for each dataset. You can view the list of all datasets seacrowd.github.io/seacrowd-catalogue.	https://github.com/SEACrowd/seacrowd-catalogue
31	SeaBench	Dataset removed	Sea bench (Dataset removed)
32	StarCoder Self-Instruct	Dataset generated by prompting starcoder to generate new instructions based on some human-written seed instructions.	https://huggingface.co/datasets/codeparrot/self-instruct-starcoder
33	Tasksource Instruct	Tasksource datasets as instructions for instruction-tuning.	https://github.com/sileod/tasksource
34	Tasksource Symbol-Tuning	Tasksource datasets converted for symbol-tuning.	https://github.com/sileod/tasksource
35	xP3x	xP3x is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more.	https://huggingface.co/datasets/Muennighoff/xP3x

Languages

This dataset consists languages from all its subsets listed in table above.

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

The following snippet shows the fields in a row in each data collection in this dataset:

[
        {"from": "user", "text": input_text.strip(), "parent": dset},
        {"from": "assistant", "text": target_text.strip(), "parent": 0},
        ...
]

with fields:

from: indicates the originator of the text in this conversation. This can be either "user" or "assistant", where "assistant" indicates the model and text will be model's response to user's text.
text: indicates text that originator wants to communicate to receiver.
parent: field indicating the parent for tracing the conversation hierarchy. Here each row contains one or more json objects indicating user-assistant interaction dialogue with text messages exchanged between them. You can leverager parent field in json object to follow the tree structure of interactions.

Here each row contains one or more json objects indicating user-assistant interaction dialogue with text messages exchanged between them. You can leverager parent field in json object to follow the tree structure of interactions.

Downloading Dataset

You can load the entire dataset by using the following code:

import os
from datasets import load_dataset

dataset = load_dataset(
    "DataProvenanceInitiative/commercial_licenses_and_terms",
    split="train",
    num_proc = os.cpu_count(),
    revision="main", 
)

You can load a specific dataset subset such as Book Summaries using the following code:

import os
from datasets import load_dataset

subset = load_dataset(
    "json",
    split="train",
    num_proc = os.cpu_count(),
    revision="main", 
    data_files="./book_summaries/*.jsonl"
)

Data Splits

[More Information Needed]

Dataset Creation

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@article{longpre2023data,
  title={The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing \& Attribution in AI},
  author={Longpre, Shayne and Mahari, Robert and Chen, Anthony and Obeng-Marnu, Naana and Sileo, Damien and Brannon, William and Muennighoff, Niklas and Khazam, Nathan and Kabbara, Jad and Perisetla, Kartik and others},
  journal={arXiv preprint arXiv:2310.16787},
  year={2023}
}